dCat: Dynamic Cache Management for Efficient, Performance-sensitive Infrastructure-as-a-Service

Abstract

In the modern multi-tenant cloud, resource sharing increases utilization but causes performance interference between tenants. More generally, performance isolation is also relevant in any multi-workload scenario involving shared resources. Last level cache (LLC) on processors is shared by all CPU cores in x86, thus the cloud tenants inevitably suffer from the cache flush by their noisy neighbors running on the same socket. Intel Cache Allocation Technology (CAT) provides a mechanism to assign cache ways to cores to enable cache isolation, but its static configuration can result in underutilized cache when a workload cannot benefit from its allocated cache capacity, and/or lead to sub-optimal performance for workloads that do not have enough assigned capacity to fit their working set. In this work, we propose a new dynamic cache management technology (dCat) to provide strong cache isolation with better performance. For each workload, we target a consistent, minimum performance bound irrespective of others on the socket and dependent only on its rightful share of the LLC capacity. In addition, when there is spare capacity on the socket, or when some workloads are not obtaining beneficial performance from their cache allocation, dCat dynamically reallocates cache space to cache-intensive workloads. We have implemented dCat in Linux on top of CAT to dynamically adjust cache mappings. dCat requires no modifications to applications so that it can be applied to all cloud workloads. Based on our evaluation, we see an average of 25% improvement over shared cache and 15.7% over static CAT for selected, memory intensive, SPEC CPU2006 workloads. For typical cloud workloads, with Redis we see 57.6% improvement (over shared LLC) and 26.6% improvement (over static partition) and with ElasticSearch we see 11.9% improvement over both.